Ludus-FastMCP: A Model Context Protocol server for automating Ludus v1 and v2 cyber range environments through AI assistants

by tjnull · MCP Server · ★ 84

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install Ludus-FastMCP

pipx install git+https://github.com/tjnull/Ludus-FastMCP
git clone https://github.com/tjnull/Ludus-FastMCP

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 71/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars84
Forks12
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score71/100
Last Updated2026-08-05
Created2025-12-19
Platformsmcp, python
Est. Tokens~15k

About Ludus-FastMCP

Ludus FastMCP A Model Context Protocol (MCP) server for automating Ludus cyber range environments through AI assistants written in Python. Overview Ludus FastMCP enables AI-powered management of Ludus cyber ranges through natural language commands.

Compatible Skills

These tools work well together with Ludus-FastMCP for enhanced workflows:

  • VidTrainPrep — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • bloodhound_mcp — semantic(0.30)+same_lang+similar_pop+shared_platform (45%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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